> Markdown version of [/jobs/ext/4114954-data-platform-analytics-lead](https://www.wearedevelopers.com/jobs/ext/4114954-data-platform-analytics-lead). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data & Platform Analytics Lead - **Company:** Puzzel - **Location:** London, United Kingdom - **Experience:** Expert - **Contract:** Internship / Graduate position - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Software as a Service, Data Governance, Data Infrastructure, Python (Programming Language), Raw Data, Power BI, Standard Sql, Tableau (Software), Web Analytics, Snowflake, Data Lakes, Api Management - **Published:** October 26, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=4dc08ecb0ec56715 ## About the Role * SaaS or subscription experience, in a company of any size, with a deep understanding of ARR movements, churn, cohorts, and the difference between bookings, billing and revenue. * Experience working within, or closely alongside, a finance team. * Strong SQL, and experience building a modelled reporting layer in dbt or equivalent. * Working competence with a cloud data warehouse, ideally Snowflake. * A track record of analysis that changed a decision, with examples of what you found and what happened next. * A detailed, evidence-driven approach: experience reconciling data between sources, and a sceptical eye for data that looks plausible but is wrong. * Experience getting senior people from different functions to agree a definition, then holding the line on it. * The confidence to challenge how data is handled, and the drive to work independently as a self-starter. * Daily use of AI tools in your work, with examples of what you built and how you checked it. * The ability to explain a number, and its caveats, to a board member in a few lines. The nice to haves * Experience with Abacum, which we would particularly welcome. * A BI tool such as Power BI or Tableau. * Snowflake administration: performance, access control and cost management. * Fivetran or a similar tool, and integrating through APIs. * Python or similar scripting. * Experience in an FP&A team, or supporting external or investor reporting. * Experience of a system migration or historical data load. * Data governance experience, including handling confidential financial and personal data. ## Description Puzzel's leaders need numbers they can rely on, but today our data differs from one department to the next. You will be our data wrangler. You control what goes into our Snowflake data lake, make sure what the business needs is going in, and organise it so every department extracts it the same way, in the same language. You remove ambiguity, such as what counts as a customer, and you own the definitions. Our AI transformation lead builds on top of this data, so you are laying the foundations. You will report to our CFO and work across the finance team, the customer organisation, AI transformation and anyone who owns data in the business. You think critically about what has happened and stay curious about it. When you spot an anomaly, you act on it. You rely on evidence, not gut feel, you are confident enough to challenge how we handle data, and you work independently. AI is part of how we work. You will use it every day to build faster and catch problems earlier, and the data you own is what our AI tools draw on, so it has to be right. * The data platform. Take ownership of our Snowflake instance: what goes in, how it is structured, and access, cost, performance and security. * The pipelines. Run and extend our Fivetran and API integrations so data from every function lands reliably, with monitoring and alerts when something breaks. * The definitions. Agree what each core metric means (customers, ARR, churn, pipeline, headcount, usage) with the people who own the data. Publish one definition, name one owner, and escalate where you cannot agree. * The data model. Extend our dbt models into a clear, documented schema, from raw data through to a governed reporting layer. * Reconciliation and quality. Tie reports back to source systems, including financial ledgers. Spot anomalies, trace them to the cause and act on them. A number that does not reconcile does not go out. * The reporting layer. Build and run the models, dashboards and monthly management pack for the board, executive team and directors, on a fixed calendar. * The explanation. Each month, explain what moved and why across revenue, ARR, churn, pipeline, margin and usage, and answer the questions the data raises before anyone asks. * Adoption. Get definitions and reports to the people who need them, in a form they can use, so teams stop building their own versions.